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Accelerating scientific breakthroughs with an AI co-scientist

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Re: Accelerating scientific breakthroughs with an AI co-scientist

#81

Post by tsumnia »

dekhn wrote: post #80
crypto420 wrote: post #11

I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…

So, I've been reading Google research papers for decades now and also worked there for a decade and wrote a few papers of my own. When google publishes papers, they tend to juice the results significance (google is not the only group that does this, but they are pretty egregious). You need to be skilled in the field of the paper to be able to pare away the exceptional claims. A really good example is https://spectrum…

Remember Google is a publicly traded company, so everything must be reviewed to "ensure shareholder value". Like dekhn said, its impressive, but marketing wants more than "impressive".

Re: Accelerating scientific breakthroughs with an AI co-scientist

#82

Post by tsumnia »

akomtu wrote: post #60
waynenilsen wrote: post #21

it seems that humans may become the hands of the AI before the robots are ready mechanical turk, but for biology

That's the Quake version of the machine civilization: machines make the decisions, but use chunks of humans to improve their unholy machinery. The alternative Doom version is the opposite: humans make the decisions, but they are blended in an unholy way into the machines.

Now do the Warhammer 40k version :D

Re: Accelerating scientific breakthroughs with an AI co-scientist

#83

Post by dekhn »

tsumnia wrote: post #81
dekhn wrote: post #80

Earlier quoted context omitted.

So, I've been reading Google research papers for decades now and also worked there for a decade and wrote a few papers of my own. When google publishes papers, they tend to juice the results significance (google is not the only group that does this, but they are pretty egregious). You need to be skilled in the field of the paper to be able to pare away the exceptional claims. A really good example is https://spectrum…

Remember Google is a publicly traded company, so everything must be reviewed to "ensure shareholder value". Like dekhn said, its impressive, but marketing wants more than "impressive".

This is true for public universities and private universities; you see the same thing happening in academic papers (and especially the university PR around the paper)

Re: Accelerating scientific breakthroughs with an AI co-scientist

#84

Post by shpongled »

petra wrote: post #76
shpongled wrote: post #42

Earlier quoted context omitted.

We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.

So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?

I wouldn't say it's an engineering problem. Biology and pharmacology are very complex with lots of curveballs, and each experiment is often different and not done enough to warrant full engineering-scale optimization (although this is sometimes the case!).

Re: Accelerating scientific breakthroughs with an AI co-scientist

#85

Post by baq »

rtkwe wrote: post #54
klipt wrote: post #37

Earlier quoted context omitted.

Only two years since chatGPT was released and AI at the level of "impressive high school sophomore" is already blasé.

Suggesting "maybe try this known inhibitor in other cell lines" isn't exactly novel information though. It'd be more impressive and useful if it hadn't had any published information about working as a cancer inhibitor before. People are blasé about it because it's not really beating the allegations that it's just a very fancy parrot when the highlight of it's achievements is to say try this known inhibitor with these…

A couple years ago even suggesting that a computer could propose anything at all was sci-fi. Today a computer read the whole internet, suggested a place to look at and experiments to perform and… ‘not impressive enough’. Oof.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#86

Post by kridsdale1 »

petra wrote: post #76
shpongled wrote: post #42

Earlier quoted context omitted.

We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.

So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?

It also seems to be a financial problem of getting VC funds to run trials to appease regulators. Even if you’ve already seen results in a lab or other country.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#87

Post by kridsdale1 »

dekhn wrote: post #74
Teever wrote: post #62

Earlier quoted context omitted.

Any idea why they're they so expensive?

I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built ( https://www.transcriptic.com/ ). So my scope could be automated by the workcell automation components (robot arms, motors, conveyors, etc). When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educ…

That’s similar to how Google won in distributed systems. They used cheap PCs in shipping containers when everyone else was buying huge expensive SUN etc servers.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#88

Post by coherentpony »

celltalk wrote: post #41
ttpphd wrote: post #31

Earlier quoted context omitted.

It's almost like scientists are doing something more than a random search over language.

I do hallucinate a better future as well.

It bothers me that the word 'hallucinate' is used to describe when the output of a machine learning model is wrong.

In other fields, when models are wrong, the discussion is around 'errors'. How large the errors are, their structural nature, possible bounds, and so forth. But when it's AI it's a 'hallucination'. Almost as if the thing is feeling a bit poorly and just needs to rest and take some fever-reducer before being correct again.

It bothers me. Probably more than it should, but it does.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#89

Post by ein0p »

dekhn wrote: post #80
crypto420 wrote: post #11

I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…

So, I've been reading Google research papers for decades now and also worked there for a decade and wrote a few papers of my own. When google publishes papers, they tend to juice the results significance (google is not the only group that does this, but they are pretty egregious). You need to be skilled in the field of the paper to be able to pare away the exceptional claims. A really good example is https://spectrum…

That applies to absolutely everyone. Convenient results are highlighted, inconvenient are either not mentioned or de-emphasized. You do have to be well read in the field to see what the authors _aren't_ saying, that's one of the purposes of being well-read in the first place. That is also why 100% of science reporting is basically disinformation - journalists are not equipped with this level of nuanced understanding.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#90

Post by j_timberlake »

hinkley wrote: post #78
crypto420 wrote: post #11

I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…

> in silico discovery Oh I don’t like that. I don’t like that at all.

Don't worry, it takes about 10 years for drugs to get approved, AIs will be superintelligent long before the government gives you permission to buy a dose of AI-developed drugs.
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